Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

On orbit validation of solar sailing control laws with thin-film spacecraft

Many innovative approaches to solar sail mission and trajectory design have been proposed over the years, but very few ever have the opportunity to be validated on orbit with real spacecraft. Thin- Film Spacecraft/Lander/Rovers (TF-SL Rs) are a new class of very low cost, low mass space vehicle which are ideal for inexpensively and quickly testing in flight new approaches to solar sailing. This paper describes using TF- SLR based micro solar sails to implement a generic solar sail test bed on orbit. TF -SLRs are high area- to-mass ratio (A/m) spacecraft developed for very low cost consumer and scientific deep space missions. Typically based on a 5 μm or thinner metalised substrate, they include an integrated avionics and payload system -on-chip (SoC) die bonded to the substrate with passive components and solar cells printed or deposited by Metal Organic Chemical Vapour Deposition (MOCVD). The avionics include UHF/S- band transceivers, processors, storage, sensors and attitude control provided by integrated magnetorquers and reflectivity control devices. Resulting spacecraft have a typical thickness of less than 50 μm, are 80 mm in diameter, and have a mass of less than 100 mg resulting in sail loads of less than 20 g/m 2 . TF -SLRs are currently designed for direct dispensing in swarms from free flying 0.5U Interplanetary CubeSats or dispensers attached to launch vehicles. Larger 160 mm, 320 mm and 640 mm diameter TF -SLRs utilizing a CubeSat compatible TWIST deployment mechanism that maintains the high A/m ratio are also under development. We are developing a mission to demonstrate the utility of these devices as a test bed for experimenting with a variety of mission designs and control laws. Batches of up to one hundred TF- SLRs will be released on earth escape trajectories, with each batch executing a heterogeneous or homogenous mixture of control laws and experiments. Up to four releases at different points in orbit are currently envisaged with experiments currently being studied in MATLAB and GMA T including managing the rate of separation of individual spacecraft, station keeping and single deployment/substantially divergent trajectory development. It is also hoped to be able to demonstrate uploading new experiment designs while in orbit and to make this capability available to researchers around the world. A suitable earth escape mission is currently being sought and it is hoped the test bed could be on orbit in 2017/18

A tutorial task and tertiary courseware model for collaborative learning communities

RAED provides a computerised infrastructure to support the development and administration of Vicarious Learning in collaborative learning communities spread across multiple universities and workplaces. The system is based on the OASIS middleware for Role-based Access Control. This paper describes the origins of the model and the approach to implementation and outlines some of its benefits to collaborative teachers and learners

The superheated Melting of Grain Boundary

Based on a model of the melting of Grain Boundary (GB), we discuss the possibility of the existence of superheated GB state. A Molecular Dynamics simulation presented here shows that the superheated GB state can realized in the high symmetric tilt GB. Whether the sizes of liquid nuclei exceed a critical size determined the superheating grain boundary melting or not. Our results also indicate that the increase of melting point due to pressure is smaller than the superheating due to nucleation mechanism.Comment: Accepted by PRB, 7 pages and 5 figure

Video-based online face recognition using identity surfaces

Recognising faces across multiple views is more challenging than that from a fixed view because of the severe non-linearity caused by rotation in depth, self-occlusion, self-shading, and change of illumination. The problem can be related to the problem of modelling the spatiotemporal dynamics of moving faces from video input for unconstrained live face recognition. Both problems remain largely under-developed. To address the problems, a novel approach is presented in this paper. A multi-view dynamic face model is designed to extract the shape-and-pose-free texture patterns of faces. The model provides a precise correspondence to the task of recognition since the 3D shape information is used to warp the multi-view faces onto the model mean shape in frontal-view. The identity surface of each subject is constructed in a discriminant feature space from a sparse set of face texture patterns, or more practically, from one or more learning sequences containing the face of the subject. Instead of matching templates or estimating multi-modal density functions, face recognition can be performed by computing the pattern distances to the identity surfaces or trajectory distances between the object and model trajectories. Experimental results depict that this approach provides an accurate recognition rate while using trajectory distances achieves a more robust performance since the trajectories encode the spatio-temporal information and contain accumulated evidence about the moving faces in a video input

Temperature Dependent Empirical Pseudopotential Theory For Self-Assembled Quantum Dots

We develop a temperature dependent empirical pseudopotential theory to study the electronic and optical properties of self-assembled quantum dots (QDs) at finite temperature. The theory takes the effects of both lattice expansion and lattice vibration into account. We apply the theory to the InAs/GaAs QDs. For the unstrained InAs/GaAs heterostructure, the conduction band offset increases whereas the valence band offset decreases with increasing of the temperature, and there is a type-I to type-II transition at approximately 135 K. Yet, for InAs/GaAs QDs, the holes are still localized in the QDs even at room temperature, because the large lattice mismatch between InAs and GaAs greatly enhances the valence band offset. The single particle energy levels in the QDs show strong temperature dependence due to the change of confinement potentials. Because of the changes of the band offsets, the electron wave functions confined in QDs increase by about 1 - 5%, whereas the hole wave functions decrease by about 30 - 40% when the temperature increases from 0 to 300 K. The calculated recombination energies of exciton, biexciton and charged excitons show red shifts with increasing of the temperature, which are in excellent agreement with available experimental data